Evidence map›Paper›PMID 30397749›Full record

ArticleEnvironmental science and pollution research international2019

Duckweed biomarkers for identifying toxic water contaminants?

Paul Ziegler, Kandregula Sowjanya Sree, Klaus-Jürgen Appenroth

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
6.3field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 62 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 2 countries.

Paul ZieglerDepartment of Plant Physiology, University of Bayreuth, Universitätsstr. 30, 95440, Bayreuth, Germany. paul.ziegler@uni-bayreuth.de.ORCID http://orcid.org/0000-0002-2486-9048
Kandregula Sowjanya SreeDepartment of Environmental Science, Central University of Kerala, Tejaswini Hills, Periye, 671316, India.
Klaus-Jürgen AppenrothMatthias-Schleiden Institute, Department of Plant Physiology, Friedrich-Schiller-University Jena, Dornburger Str. 159, 07743, Jena, Germany.
Central University of Kerala · INFriedrich Schiller University Jena · DEUniversity of Bayreuth · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surface or ground waters can be contaminated with numerous toxic substances. The duckweeds Lemna minor and Lemna gibba are widely used for assaying waterborne toxicity to higher plants in terms of growth inhibition and photosynthetic pigment reduction. These tests cannot, however, in themselves determine the nature of the agents responsible for toxicity. Morphological, developmental, physiological, biochemical, and genetic responses of duckweeds to exposure to toxic water contaminants constitute biomarkers of toxic effect. In principle, the very detection of these biomarkers should enable the contaminants having elicited them (and being responsible for the toxicity) to be identified. However, in practice, this is severely compromised by insufficient specificity of biomarkers for their corresponding toxicants and by the lack of documentation of biomarker/toxin relationships. The present contribution illustrates the difficulties of using known water contaminant-related duckweed biomarkers to identify toxins, and discusses possibilities for achieving this goal.

Indexed as

BiomarkersAraceaeWater Pollutants, ChemicalBiomarkersWater Pollutants, ChemicalBiomarkersDuckweedsLemna gibbaLemna minorToxicant identificationToxicity testingWater contaminants

Identifiers

PMID30397749
OpenAlexW2899844289

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.